Required dependencies: python-captcha, opencv, python-tensorflow (CPU or GPU)
python generate-audio-captcha.py --length 8 --symbols symbols.txt --count 3200 --output-dir training-images
This generates 3200 audio captchas with 8 characters per captcha, using the
set of symbols in the symbols.txt file with the help of gTTS service. The captchas are stored in the folder
training-images, which is created if it doesn't exist. The names of the captcha images
are scrambled if passed the option.
Without the --scramble option, the name of the image is the captcha text.
To train and validate a neural network, we need two sets of data: a big training set, and a smaller validation set. The network is trained on the training set, and tested on the validation set, so it is very important that there are no audio that are in both sets.
To generate the training data, the "ground truth" classification for each
training example audio must be known. This means that for training, the names
of the captchas cannot be scrambled, because otherwise the training process
has no way to check if the answer from the CNN for some captcha is right or
wrong! Make sure not to use the --scramble option when generating the
training or validation datasets.
python train.py --width 128 --height 64 --length 8 --symbols symbols.txt --batch-size 4 --epochs 2 --output-model char8e6bs4 --train-dataset training_data --validate-dataset validation_data
Train the neural network for 2 epochs on the data specified. One epoch is one pass through the full dataset.
The suggested training dataset size for the initial training for captcha length of 4 symbols is 20000 images, with a validation dataset size of 4000 images.
python classify.py --model-name char8e6bs4 --captcha-dir test_data/ --output output.txt --symbols symbols.txt
With --model-name test the classifier script will look for a model called
test.json with weights test.h5 in the current directory, and load the model
up.
The classifier runs all the images in --captcha-dir through the model, and
saves the file names and the model's guess at captcha contained in the image in
the --output file.
Base code taken and modified from: https://gitlab.com/andrewwja/captcha-demo